Invariant-First AI: Compression-Based Architectures for Coherent and Energy-Efficient Artificial Intelligence

Abstract

Contemporary artificial intelligence systems achieve impressive performance at the cost of extreme computational, energetic, and data inefficiency. Large-scale models rely on brute-force accumulation of instances rather than principled abstraction, resulting in instability, hallucination, and escalating resource demands. Drawing on the Unified Consciousness Substrate Theory (UCST) and internal project research (Memory Bank), this paper proposes an alternative design paradigm: invariant-first artificial intelligence. Inspired by single-timeline compression models of reality, we argue that coherent intelligence emerges from the preservation of compressed structural constraints rather than exhaustive memory of events. We formalize a three-layer AI architecture that separates invariant constraint learning from contextual modeling and linguistic expression, demonstrate how this approach reduces computational cost and instability, and map the framework onto information theory and thermodynamics. Implications for AI safety, alignment, and long-term sustainability are discussed.

Keywords: artificial intelligence, information compression, invariants, coherence, free energy, UCST

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Archive Link: https://doi.org/10.5281/zenodo.18393507